Brain-computer interface: from lab to real scene_Journal of Biomedical Engineering

Authors：

 YAO Dezhong ^1,2,3

1. School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, P.R.China;
2. Sichuan Institute for Brain Science and Brain-Inspired Intelligence, Chengdu 611731, P.R.China;
3. Research Unit of NeuroInformation, 2019RU035, Chinese Academy of Medical Sciences, Chengdu 611731, P.R.China;

Corresponding author：

YAO Dezhong, Email: dyao@uestc.edu.cn

Keywords：

brain-computer interface; real scene application; challenges for future

DOI：

10.7507/1001-5515.202105091

Video：

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Brain-computer interface (BCI) can be summarized as a system that uses online brain information to realize communication between brain and computer. BCI has experienced nearly half a century of development, although it now has a high degree of awareness in the public, but the application of BCI in the actual scene is still very limited. This collection invited some BCI teams in China to report their efforts to promote BCI from laboratory to real scene. This paper summarizes the main contents of the invited papers, and looks forward to the future of BCI.

Citation： YAO Dezhong. Brain-computer interface: from lab to real scene. Journal of Biomedical Engineering, 2021, 38(3): 405-408. doi: 10.7507/1001-5515.202105091 Copy

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2.	Schmidt E M. Single neuron recording from motor cortex as a possible source of signals for control of external devices. Ann Biomed Eng, 1980, 8(4-6): 339-349.
3.	Nowlis D P, Kamiya J. The control of electroencephalographic alpha rhythms through auditory feedback and the associated mental activity. Psychophysiology, 1970, 6(4): 476-484.
4.	Vidal J J. Toward direct brain-computer communication. Annu Rev Biophys Bioeng, 1973, 2: 157-180.
5.	尧德中. 脑-机接口: 从神奇到现实转变. 中国生物医学工程学报, 2014, 33(6): 641-643.
6.	Johns Hopkins University. Quadriplegic patient uses brain signals to feed himself with two advanced prosthetic arms[EB/OL]. (2020-12-29) [2021-05-28]. https: //medicalxpress. com/news/2020-12-quadriplegic-patient-brain-advanced-prosthetic. html.
7.	Willett F R, Avansino D T, Hochberg L R, et al. High-performance brain-to-text communication via handwriting. Nature, 2021, 593(7858): 249-254.
8.	Wolpaw J R, Birbaumer N, McFarland D J, et al. Brain-computer interfaces for communication and control. Clin Neurophysiol, 2002, 113(6): 767-791.
9.	张力新, 陈小翠, 陈龙, 等. 基于运动想象脑-机接口的协同策略研究. 生物医学工程学杂志, 2021, 38(3): 409-416.
10.	左词立, 毛盈, 刘倩倩, 等. 不同复杂度汉字模式下运动想象脑机接口性能研究. 生物医学工程学杂志, 2021, 38(3): 417-424, 454.
11.	李嘉莹, 赵丽, 边琰, 等. 电刺激辅助下肢运动想象特征分类以增强康复训练研究. 生物医学工程学杂志, 2021, 38(3): 425-433.
12.	Xie J, Peng M, Lu J, et al. Enhancement of event-related desynchronization in motor imagery based on transcranial electrical stimulation. Front Hum Neurosci, 2021, 15: 635351.
13.	田贵鑫, 丁鹏, 龚安民, 等. 脑机接口中运动想象的执行与能力的评估和提高方法. 生物医学工程学杂志, 2021, 38(3): 434-446.
14.	汪佳衡, 王跃明, 姚林. 基于滤波器组长短时记忆网络的脑电信号情绪识别. 生物医学工程学杂志, 2021, 38(3): 447-454.
15.	蔡梓良, 郭苗苗, 杨新生, 等. 基于最大分类器差异域对抗方法的跨被试脑电情绪识别研究. 生物医学工程学杂志, 2021, 38(3): 455-462.
16.	孙劲松, 肖晓琳, 孟佳圆, 等. 脑-机接口中错误相关电位的解码算法研究. 生物医学工程学杂志, 2021, 38(3): 463-472.
17.	张锐, 刘家俊, 陈明明, 等. 基于小波变换—集合经验模态分解的单通道脑电信号眼电伪迹自动去除研究. 生物医学工程学杂志, 2021, 38(3): 473-482.
18.	陈小刚, 李坤. 基于增强现实脑机接口和计算机视觉的机械臂控制系统. 生物医学工程学杂志, 2021, 38(3): 483-491.
19.	黄海云, 蔡跃新, 冯学技, 等. 基于脑电信号的耳鸣患者静息态频谱图及注意力研究. 生物医学工程学杂志, 2021, 38(3): 492-497.
20.	刘蒙蒙, 徐桂芝, 于洪丽, 等. 经颅直流电刺激下脑卒中患者康复期脑功能网络特性研究. 生物医学工程学杂志, 2021, 38(3): 498-506, 511.
21.	高上凯, 吕宝粮, 张丽清. 脑-计算机交互研究前沿. 上海: 上海交通大学出版社, 2021.
22.	秦云, 刘铁军, 尧德中. 脑器交互学——脑与外界协同的新学科. 生物医学工程学杂志, 2021, 38(3): 507-511.
23.	Yao D, Zhang Y, Liu T, et al. Bacomics: a comprehensive cross area originating in the studies of various brain-apparatus conversations. Cogn Neurodyn, 2020, 14(4): 425-442.
24.	Gao D, Long S, Yang H, et al. SWS brain-wave music may improve the quality of sleep: An EEG study. Front Neurosci, 2020, 14: 67.
25.	Ma T, Li H, Yang H, et al. The extraction of motion-onset VEP BCI features based on deep learning and compressed sensing. J Neurosci Methods, 2017, 275: 80-92.
26.	Zhu X, Li P, Li C, et al. Separated channel convolutional neural network to realize the training free motor imagery BCI systems. Biomed Signal Process Control, 2019, 49: 396-403.

1. Fetz E E. Operant conditioning of cortical unit activity. Science, 1969, 163(3870): 955-958.
2. Schmidt E M. Single neuron recording from motor cortex as a possible source of signals for control of external devices. Ann Biomed Eng, 1980, 8(4-6): 339-349.
3. Nowlis D P, Kamiya J. The control of electroencephalographic alpha rhythms through auditory feedback and the associated mental activity. Psychophysiology, 1970, 6(4): 476-484.
4. Vidal J J. Toward direct brain-computer communication. Annu Rev Biophys Bioeng, 1973, 2: 157-180.
5. 尧德中. 脑-机接口: 从神奇到现实转变. 中国生物医学工程学报, 2014, 33(6): 641-643.
6. Johns Hopkins University. Quadriplegic patient uses brain signals to feed himself with two advanced prosthetic arms[EB/OL]. (2020-12-29) [2021-05-28]. https: //medicalxpress. com/news/2020-12-quadriplegic-patient-brain-advanced-prosthetic. html.
7. Willett F R, Avansino D T, Hochberg L R, et al. High-performance brain-to-text communication via handwriting. Nature, 2021, 593(7858): 249-254.
8. Wolpaw J R, Birbaumer N, McFarland D J, et al. Brain-computer interfaces for communication and control. Clin Neurophysiol, 2002, 113(6): 767-791.
9. 张力新, 陈小翠, 陈龙, 等. 基于运动想象脑-机接口的协同策略研究. 生物医学工程学杂志, 2021, 38(3): 409-416.
10. 左词立, 毛盈, 刘倩倩, 等. 不同复杂度汉字模式下运动想象脑机接口性能研究. 生物医学工程学杂志, 2021, 38(3): 417-424, 454.
11. 李嘉莹, 赵丽, 边琰, 等. 电刺激辅助下肢运动想象特征分类以增强康复训练研究. 生物医学工程学杂志, 2021, 38(3): 425-433.
12. Xie J, Peng M, Lu J, et al. Enhancement of event-related desynchronization in motor imagery based on transcranial electrical stimulation. Front Hum Neurosci, 2021, 15: 635351.
13. 田贵鑫, 丁鹏, 龚安民, 等. 脑机接口中运动想象的执行与能力的评估和提高方法. 生物医学工程学杂志, 2021, 38(3): 434-446.
14. 汪佳衡, 王跃明, 姚林. 基于滤波器组长短时记忆网络的脑电信号情绪识别. 生物医学工程学杂志, 2021, 38(3): 447-454.
15. 蔡梓良, 郭苗苗, 杨新生, 等. 基于最大分类器差异域对抗方法的跨被试脑电情绪识别研究. 生物医学工程学杂志, 2021, 38(3): 455-462.
16. 孙劲松, 肖晓琳, 孟佳圆, 等. 脑-机接口中错误相关电位的解码算法研究. 生物医学工程学杂志, 2021, 38(3): 463-472.
17. 张锐, 刘家俊, 陈明明, 等. 基于小波变换—集合经验模态分解的单通道脑电信号眼电伪迹自动去除研究. 生物医学工程学杂志, 2021, 38(3): 473-482.
18. 陈小刚, 李坤. 基于增强现实脑机接口和计算机视觉的机械臂控制系统. 生物医学工程学杂志, 2021, 38(3): 483-491.
19. 黄海云, 蔡跃新, 冯学技, 等. 基于脑电信号的耳鸣患者静息态频谱图及注意力研究. 生物医学工程学杂志, 2021, 38(3): 492-497.
20. 刘蒙蒙, 徐桂芝, 于洪丽, 等. 经颅直流电刺激下脑卒中患者康复期脑功能网络特性研究. 生物医学工程学杂志, 2021, 38(3): 498-506, 511.
21. 高上凯, 吕宝粮, 张丽清. 脑-计算机交互研究前沿. 上海: 上海交通大学出版社, 2021.
22. 秦云, 刘铁军, 尧德中. 脑器交互学——脑与外界协同的新学科. 生物医学工程学杂志, 2021, 38(3): 507-511.
23. Yao D, Zhang Y, Liu T, et al. Bacomics: a comprehensive cross area originating in the studies of various brain-apparatus conversations. Cogn Neurodyn, 2020, 14(4): 425-442.
24. Gao D, Long S, Yang H, et al. SWS brain-wave music may improve the quality of sleep: An EEG study. Front Neurosci, 2020, 14: 67.
25. Ma T, Li H, Yang H, et al. The extraction of motion-onset VEP BCI features based on deep learning and compressed sensing. J Neurosci Methods, 2017, 275: 80-92.
26. Zhu X, Li P, Li C, et al. Separated channel convolutional neural network to realize the training free motor imagery BCI systems. Biomed Signal Process Control, 2019, 49: 396-403.

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